Abstract: Causality is central to neuroscience. For example, we might ask about the causal effect of a neuron on another neuron, or its influence on perception, action, or cognition. Moreover, any medical approaches aim at producing a causal effect ? effecting improvements for patients. Randomized controlled trials (RCTs) are the gold standard to establish causality, but they are not always practical. For example, while we can electrically or optogenetically activate entire areas, large-scale targeted stimulation of individual neurons is hard. Other ways of establishing causality are problematic: if we observe a correlation it is hard to know its cause. The problem is confounding: there are variables that we do not record that affect the variables we do. This also renders model comparisons problematic ? a causally wrong model with few parameters may well fit the observed data better than a causally correct one with many parameters. We thus need data analysis tools that allow authoritatively asking causal questions without the need for random perturbation experiments. Just like neuroscience now, the field of econometrics once focused on correlations. But since the 1980s, empirical economics has undergone a so-called credibility revolution, requiring the development of rigorous methods to establish causality. Several successful methods have emerged to become the workhorses of empirical economics. The idea underlying these methods is that if one can observe variables that approximate random perturbations, then one can still discover causal relations. This is what economists call a quasi-experiment. We here propose to carry over such quasi-experimental techniques to neuroscience. For example in neuroscience, if there is a random variable that affects only one neuron, then any activity in other neurons correlated with that variable must be causally affected by the neuron. Another famous quasi- experimental method is regression discontinuity design (RDD). This approach effectively uses the noise introduced at the threshold to identify causal relations. Importantly, such techniques have, thanks to decades of research in econometrics, very well understood statistical properties. These approaches promise to considerably enrich the approaches towards causality we have in neuroscience. We have a strong interdisciplinary team, spanning economics, experimental, and computational neuroscience, collaborating on adapting these quasi-experimental techniques to problems in neuroscience through a combination of machine learning and domain-specific engineering. This promises to be a major advance relative to current techniques that generally approach causality in neuroscience through model comparison.